1. Technical Field of the Invention
This invention most generally relates to the simulation and testing of smart antenna systems. More particularly, it pertains to methods and apparatus for verifying the functionality and performance of a smart antenna processor by simulating multipath signals and co-channel signals received at a multi-sensor antenna array, including the coordinated effects of delay spread, Doppler spread, and angular spread for all the sensors of the antenna array.
2. Background of the Invention
The personal communication services industry in the wireless market has seen a substantial growth, particularly in the cellular telephone segment. The deregulation of the telecommunications industry has fueled the fire of this rapid expansion, and pushed the technological envelope to new heights. The increased demand requires that innovative systems be developed that allow for more users, greater coverage, improved reception, lower costs, less power, and geo-location ability. And, for those that are using the cellular communications for data transfer and not just voice communication, there is a desperate need for increased speed and tighter bandwidths. Some argue that the existing wire based systems are inadequate to handle the growing need for high-speed telecommunications, and that wireless systems are a viable alternative to expensive fiber optic or cable installations into every home.
In addition, conventional wire connections no longer satisfy the mobile and harried worker who requires instant access anytime and anywhere. The transition from a wire based connective society to a wireless form is also a necessary transition in some applications. Remote areas that have no access to any wiring or do not have access to a high-speed wire network require a dependable and inexpensive way to communicate. The various forms of transportation, including car, train, plane and boat also need to communicate over wireless communication means.
Thus, the need for dependable wireless systems is a necessity in order to sustain the growth of the telecommunications industry, and the high technology sector as a whole. As more and more people experience the convenience and performance capabilities of wireless communications, the consumer demand will further increase.
One of the leading technologies in the wireless market is the smart antenna. The term smart antenna has been used to describe those antenna systems with multiple antenna elements controlled by complex software algorithms that favor the user's signal or the user's location and adapt to the transmission and reception conditions to enhance performance. The geo-location advantages of obtaining data from multiple points and processing this data can be illustrated by a person's bearing ability. As the ear picks up a sound, both ears and the processing of the brain combine to allow the source of the noise to be accurately determined. Listening with a single ear does not give the necessary focus to determine the location.
Smart antenna systems use signal processing methods in conjunction with multiple antennas to achieve significant improvements in capacity and range for wireless mobile communications. Temporal and spatial filtering techniques are devised to effectively mitigate co-channel interference and remove multipaths in all its forms. There are numerous temporal/spatial processing techniques that have been proposed for uplink as well as downlink communications. Each technique is most applicable to a specific multiple access air interface and for deployment under specific operating environment.
The key role of the multiple element antenna arrays at the base station in cellular mobile radio communications is to sample, at different points in space, the waveforms propagating from users who are accessing the same communication channel. The effectiveness of spatial sampling in reducing co-channel interference and mitigating multipath effects depends on an employed signal processing technique that combines the information over time from the different antennas. The aim of any smart antenna system is to recover the user signal of interest (SOI) and produce an output with significantly improved carrier-to-interference ratio. For uplink processing, the offerings and expectations of smart antennas, however, depend on how they exploit the communication channel characteristics and remove its effect on the statistical and deterministic properties of the desired and undesired components of the waveforms incident on the base station.
There are different types of communication formats and systems that are well known in the industry. But all are subject to the same problems and limitations, namely; channel capacity, spectrum efficiency, limited range coverage, co-channel interference, multipath fading, and system complexity. Associated with these problems and limitations are the expensive and time-consuming processes of monitoring and testing the base stations to ensure that they are functioning within prescribed limits for the conditions encountered.
The basic access protocols used for mobile communications, include frequency domain multiple access (FDMA), time domain multiple access (IDMA), and code domain multiple access (CDMA). FDMA uses different frequencies to distinguish the users. In TDMA, different time slots and interleaving allow the users to be distinguished. The CDMA scheme is a spread spectrum method that uses a separate code for each user. The pseudo noise (PN) sequence spreads the spectrum over a larger bandwidth, and reduces the spectral density of the signal. A number of CDMA signals occupy the same bandwidth and appear as random noise to each other.
An additional scheme, space diversity multiple access (SDMA) uses a dynamically changing antenna to distinguish signals, using multipath signals that hit different antenna elements in the array at different times. This delay is used to differentiate the users through spatial distribution and correlation.
Spatial correlation relates to the difference between signals received by separate sensors of a multisensor array. This correlation between the data received by two or more sensors can be measured at the same or at different time instants. Thus, spatial correlation depends on the temporal correlation of received waveforms as well as other variables related to spatial dimension, such as: the narrow and broadband properties of the transmitted signal, the array sensor spacing, the mutual coupling between the adjacent and distant sensors, the height of the antennas and their polarization, the array manifold and the omni-directional features of the array sensors, and the channel dynamics including Doppler, delay, and angular spreads.
Temporal correlation is the correlation between two data samples at the same or different time instant, so it is a correlation across time and only a function of the statistical properties of the transmitted waveforms. Strong correlation makes it easier to differentiate between directional and non-directional components of the data. The non-directional components, .such as thermal noise, are often assumed to be independent from one sensor to another.
Directional components are those generated by near or far transmitters and follow propagation, attenuation, scattering, diffraction, and refraction laws before reaching the receiver. The directional components contain information about the transmitting sources, and this information can be extracted with proper processing in the smart antenna. Thus, the working environment of the antenna contributes significantly to the transmission and reception characteristics. The environment determines the amount of multipath and interferer signals that are introduced in the antenna reception.
Multipath propagation refers to those signals arriving at a receiving antenna as a result of a combination of various components from different directions. Multipath propagation effects depend upon buildings, structures, terrain conditions, and other such objects that can reflect or refract the signal, and cause the received signal power to fluctuate as a function of distance. Large reflectors, and hence long path differences, cause multipath or frequency selective fading. The amount of signal reflected depends on a number of factors, including the polarization of the incident wave, angle of arrival, carrier frequency, and the relative permitivity of the surface.
The speed of radio waves is determined by the speed of light divided by the dielectric constant of the medium, which can be roughly calculated for air by C=(3.times.10.sup.8)/(1).sup.1/2. The radio waves are subject to reflection, refraction, absorption, and diffraction, that changes the way in which the incident waves may be perceived. Reflection off a conductive surface can be specular or mirror-like, if the reflecting surface is flat. The reflection may also be diffuse if the surface is not flat and the waves are scattered. Refraction occurs when the dielectric constant changes and the angle of incidence upon the refracting medium cause the angle to change because the speed of the wave changes. Absorption refers to the refractive effects of water and gases in the air, and for frequencies of less than 1 GHz, the effects are negligible. Diffraction occurs when the radio waves encounter an object, and curve around the object if the object size is comparable to the wavelength or bend around the object if the object is much larger than the wavelength.
The radio waves are also subject to path losses or attenuation. The attenuation of the direct path occurs relatively slow as the receiver moves through the field, but the addition of obstacles that partially or wholly block the receiver path introduces greater attenuation. The signal received by the antenna is usually a combination of the direct and indirect paths of the transmitted signal, as well as interference signals. The direct and indirect paths taken by the transmitted signal include those directly in the line-of-sight from the mobile to the receiver, and those that involve reflection and refraction off buildings and other objects.
Each signal is strongly influenced by the distance from the transmitter and the angle of incidence at the antenna. Electromagnetic field strength varies in reverse proportion to the square of the distance. But, when atmospheric attenuation effects and the absorption of the terrain are taken into account, the attenuation can be as high as the inverse sixth power of the distance. Fading is the resultant decrease in signal power, and is the product of two variables: Rayleigh distribution and log-normal distribution.
The slow-varying quantity called log-normal distribution occurs over many different wavelengths of the carrier and is called slow fading. Slow fading is actually comprised of two components, a deterministic component and a random component. The deterministic component is a function of distance. The random component changes with the terrain and is termed shadowing.
Typically slow fading attenuation is modeled by a log normal distribution of mean power. A typical urban terrain model shows rapid amplitude variations, on the order of 20 dB, from street to street, illustrating the effects of shadowing. The equivalent suburban model shows average signal strength approximately 10 dB greater and with less rapid variations. And, the rural model shows a further 20 dB improvement. Seasonal variations of the attenuation in rural and suburban models also occur, due to the changing state of foliage on trees and plants. Both the shadowing effect and the deterministic component of path loss are encompassed in slow fading. Across the antenna array, the attenuation will not appreciably change, and is negligible, because the slow fading depends on distance and terrain conditions, and the change from one sensor element to another is miniscule in proportion to the distance traveled by the radio wave.
Rayleigh fading occurs when a receiver operates in an environment where the received signals are made up of series of reflections and refraction from a number of objects, and there is no significant path between the receiver and the transmitter. In this situation, the signals have traveled via different paths and arrive at slightly different amplitudes and phases; hence the signals can combine constructively or destructively. Rayleigh fading or fast fading, gets its name from the Rayleigh statistical distribution used to model its effects. The fast change in signal amplitude caused by the phase differences in signal components is referred to as multipath fading. And, a stationary object may observe fading where the differential phases of various multipath components change rapidly with frequency, which is called frequency-selective fading. If the fading is independent of frequency it is termed flat fading.
Multipath propagation creates the most serious threat to signal degradation in wireless communications. Signals that are reflected off other surfaces may combine with the desired signal but be out of phase. However, multipath signals that are in phase can combine and allow the received signal to be extracted. The ability to reinforce weak signals offers the advantage of extending the range of the transmitting. It offers the alternative advantage of nulling interfering signals to prevent poor signal quality and maintain a low noise floor for the received signals. While it is a potential problem, multipath is also essential for mobile communications. Without multipath processing, there would have to be far more base stations to ensure a direct line-of-sight existed between the base station and the mobile. Multipath effects are more acute in urban areas such as cities, because cities are more likely to have reflecting surfaces producing reflecting paths of varying path length. The three most significant factors for the system designer are delay spreading, Rayleigh fading, and random Doppler shifts.
Rician fading occurs where there are multiple source of reflected signals, but where an additional direct path transmission is present because of a direct line-of-sight (LOS) between the transmitter and the receiver. Examples are satellite links and air-to-ground communications. The Rician statistical distribution is a valid model where the direct and indirect path-length differences are relatively small, leading to small amount of delay spread.
Delay spread occurs when two signals follow separate paths enroute to a receiver in such a way that the distance traveled and the arrival time of the signals will be different. Due to the reflection and refraction nature of propagation signals in the area where a mobile is being used, it receives multiple and delayed copies of the same transmission, resulting in spreading of the signal in time.
Flat fading refers to the cases where latest copy of the signal arrives at the base station after a time duration that is smaller than symbol bit period. When the time difference becomes an appreciable percentage of the symbol bit period, intersymbol interference (ISI) can occur. Symbols arriving out of sequence corrupt preceding or succeeding symbols. For flat fading, which is typical in large cells under FDMA and TDMA schemes, smart antenna systems perform spatial equalization, where a single coefficient for each antenna is adjusted over time to combat co-channel type of interference.
In frequency selective fading, on the other hand, smart antenna systems must perform both temporal and spatial equalization to individually or jointly suppress the ISI as well as the co-channel interference. The higher the data rate or the greater the path length difference, the more likely the delay spread due to multipath.
The delay spread may range from a fraction of a microsecond in urban areas to 100 microseconds in hilly regions that restrict the signal bandwidth between 40 kHz and 250 kHz. This coherence bandwidth is defined as the inverse of the delay spread. In digital modulated schemes, the signal bandwidth is the inverse of the symbol duration. For coherence bandwidth, the different frequency components of the signal arrive at a receiver at different times, and the channel becomes frequency selective. Frequency selective channels are also known as dispersive channels, whereas nondispersive channels are referred to as flat fading channels. A channel becomes frequency selective when the delay spread is larger than the symbol duration and causes intersymbol interference, which may be reduced by using equalizers in TDMA and FDMA systems.
The relative motion between the base station and the mobile user introduces a Doppler frequency shift. The movement in a mobile causes the received frequency to differ from the transmitted frequency due to Doppler shift. Doppler shift is best illustrated by listening to the whistle of a moving train or the born of a moving car. The emitted sound does not vary in frequency or volume, but to a stationary listener, the sound pitch seems different. Because of the Doppler effect, the sound waves are compressed on the front edge and the waves are spread further apart behind the moving object. The frequency is slightly shifted relative to the transmitted frequency. Any movement in a mobile receiver causes it to encounter fluctuations in the received power level. This rate is called the fading rate, and depends on the transmission frequency and the velocity of the mobile unit. For example, a mobile receiver using 900 MHz frequency that is walking would produce a fading rate of 4.5 Hz, whereas the same unit in a speeding vehicle would experience a fading rate of 70 Hz.
As the received signals arrive along many paths, the relative velocity of the mobile with respect to various components of the signal differs, causing the different components to yield different Doppler shifts. This is viewed as spreading the transmitted frequency and is referred to as Doppler spread. The width of the Doppler spread in frequency domain is closely related to the rate of fluctuations in the observed signal. This Doppler shift varies with carrier frequency and mobile velocity, and affects all paths whether direct or indirect. The effect is to introduce another random frequency modulation on the top of any Rayleigh fading, thereby compounding the complicated signal processing.
An additional component, angular spread, refers to the value of the incident multipath and/or interferer signals that are measured relative to the direct line-of-sight signal in both elevation and azimuth axes in a planar array. In contrast, the angle of arrival is measured relative to the fixed axis of the array plane. A particular multipath angle of arrival can therefore be represented by the relative angle spread plus the line-of-sight angle of incidence. The sign of the signals would depend upon their incident angle on the array. Having a known reference in the direct line-of-sight signal, the corresponding multipath components can be determined.
The antennas employed on the base stations can exist in various forms, including omni-directional, directional, phased array, adaptive, and optimal. Directional antennas offer several advantages, including having greater gain in the direction in which the antenna is focused. Multi-sensor arrays such as the phased array, adaptive, and optimal offer some considerable advantages over their single element counterparts.
The multi-sensor arrays handle a larger number of callers by dividing the antenna regions into specific sectors, either fixed or variable. The fixed sector approach simply divides the 360.degree. range into a number of segments, whereas the variable sector approach dynamically changes the sector to correspond to the location of the user. This latter approach avoids handing off a user to another sector as the mobile user travels from the bounds of one sector into another. The bandwidth of the segments can vary, and be changed depending on the number of users and the signal strength and location of the user. If the signal strength is low, a larger bandwidth may be necessary to take advantage of a larger number of received signals.
The received signals can be appropriately summed to produce the information signal. However the increased bandwidth also makes the system subject to greater interference that might corrupt the information signal. The antenna system adjusts to these conditions by narrowing the bandwidth and focusing directly on the user, thus reducing or elimination much of the interference.
The many advantages of the smart antenna system include maximizing capacity, reducing co-channel interference, eliminating/reducing drop-out and hand-offs, smaller channel bandwidth, fewer base stations, and the ability to locate the user. The smart antenna systems employ sophisticated algorithms to extract the data received from multiple sensor elements and process the data according to weighing criteria and various mathematical calculations.
The directional control of the smart antenna allows the antenna to adjust the angle of incidence of the received signals. This allows the antenna to change the phase of the in-coming signals. The smart antenna takes advantage of the phase characteristics of the RF transmissions to enhance performance. It is well known that signals that combine with the same phase produce a signal with resultant amplitude of the combined signals. Similarly, signals that combine with opposite phase are nulled. By taking advantage of the directional capability of the antenna, the smart antenna amplifies the information signals and nulls the interference.
A smart antenna significantly reduces the handoff problem because the processing allows for a greater coverage of the base station range. In addition, lower signal levels can be transferred, because the smart antenna can use the directional capability to null interfering signals and reinforce several low-level signals. Also, by tracking the location of the user, the calls can be transferred not only by a low signal level, but also as the user may be entering into an obstruction that can be handled by another base station.
The economic benefit achieved by the smart antenna is dramatic. The extended coverage of the individual base stations translates into a lesser number of required stations. And, the efficient processing of the smart antennas allow for additional capacity to be handled by the smart antenna system. The processing also can force handoffs when the two or more users are creating significant co-channel interference due to their relative proximity.
The signals that are received on the different elements of the antenna array are combined to form a single output. The array response as a function of an angle is normally referred to as the array pattern or beam pattern. The process of combining the signals from different antenna elements is called beam forming, and requires weighting the individual components prior to summation. The direction in which the array has maximum response is said to be the beam pointing direction. Thus, this is the direction in which the array has maximum gain. The array pattern drops to a low value on either side of the beam pointing direction, and this point is called a null. Theoretically, the null is the position where the array response is zero, but in practice, the null position represents some value slightly larger than zero. The pattern on either side of the beam pointing direction that is between the null locations is called the main lobe.
For a given array, the beam may be pointed in different directions by mechanically moving the array, known as mechanical steering. The beam can also be steered by delaying the signals before combining them, either by phase shifting or adding a delay. Thus, even thought the main beam is pointed in a different direction, phase adjustments can place the main lobe in the same relative position to the side lobes without physically moving the antenna. Changing the gain and phase of each signal can shape the pattern as required. The phase and gain applied to the signals to shape the pattern can be extrapolated as a single complex quantity, arrived at by applying appropriate weighting to the individual signals. The ability to alter the array pattern is used to cancel interfering signals at the same frequency by positioning the null location appropriately.
Smart antenna systems manufactured for wireless communications range from switched beam to fully-adaptive, uplink only to uplink and down link, with the benefits provided by the various approaches differing accordingly. Most smart antenna systems are deployed at the base station for uplink signal processing. By equipping the base with smart antenna arrays, it is possible to fully exploit the spatial dimension in a wireless communication system. Multiple antennas provide a processing gain to increase the base station range and improve coverage. The capabilities of the antenna array to discriminate between signals based on their angles of arrival lead to reduced interference levels, which in turn can be traded for increased capacity of the system. A wide range of wireless communication systems may benefit from spatial processing including high mobility cellular systems, low mobility short range systems, and wireless local loop applications. To further increase the system capacity, spatially selective reception as well as spatially selective transmission may be adopted.
Each antenna array output represents a weighted sum of the desired signal, the undesired signals, and noise. The data-independent version of smart antennas is the switched beam antenna, which creates fixed sectors of cell sites. These sectors are divided and possibly sub-divided into a greater number of sub-sectors. Each sub-sector contains a predetermined fixed beam pattern. The center of the fixed beam possesses the greatest sensitivity, and the sensitivity decreases at the edges of the sub-sector. A mobile user is designated into a certain sector depending on the strongest signal received by the sub-sectors. The system monitors the call and switches between sub-sectors as required. The switched beam system has some limitations because the signal strength decreases as the user moves to the edges of the sub-sector. The switched beam is also not effective in nulling interfering signals that are closer to the center of the sub-sector.
The simplest form of data-dependent smart antennas is obtained by applying an appropriate complex weight to each sensor and then summing the outputs. The sensor weights are described by the equalizer weight vector. If the weight vector is adapted in real time in an optimum manner, it is possible to cancel the undesired interference and enhance the desired signal above the noise level, and as such, achieve performance which is far superior to both the single antenna case and multiple-antenna fixed beam systems.
The signals reaching the base station are collected over time at the different antenna elements of the array and are weighted and combined to mitigate the effects of multipath fading of the desired signal and reduce the co-channel interfering signals. The temporal/spatial combiner process involves second or higher order statistical moments and intensive correlation functions. It is based on the minimization of a cost function, which is different for different smart antenna systems.
The minimization is achieved either adaptively or by block processing, and aims to suppress interference and combat signal fading so as to ultimately increase the signal to interference noise ratio. The array weights are adjusted every data sample or every data block using blind or nonblind techniques, which is based on the availability of a training sequence or directional information. Adaptive techniques are devised to exploit any a priori information of the temporal structure of the desired signal or the location of its source. They rapidly track the desired and interfering signals in order to dynamically adjust the main lobe and nulling lobes of the smart antenna array pattern.
There are numerous techniques that could be employed to process the data received by the multiple antennas. "Smarter" antennas yield more performance improvement over the single receiver case. The most powerful smart antenna techniques are those that are devised for specific multiple access schemes such as FDMA, TDMA, or CDMA. These techniques are often structured to utilize both the temporal and the spatial characteristics of the signals over time and space. They all aim to provide some sort of temporal/spatial equalization to mitigate the effects of multipath and co-channel interference.
In principle, spatial equalization is primarily concerned with the removal of the co-channel interferers based on their angles-of-arrival, which are different from that of the signal of interest, as well as their uncorrelation with the SOI. The temporal equalization, on the other hand, primarily targets the multipath and mitigates its effect by utilizing the coherence properties of the delayed versions of the signal. The spatial and temporal equalization can be performed independently or may be combined under one optimization criterion, which can be formulated consistent with a specific multiple access scheme.
The multiple elements of the antenna and the subsequent processing allow the location of the transmitted signal to be determined in a highly calibrated and refined system.
This geo-location capability of the smart antenna is a requirement under recent telecommunications law. According to this recent legislation, service providers are under an obligation to implement a geo-location platform, whereby the location of users can be ascertained. The reasoning behind the legislation is to allow emergency callers of 911 to be located quickly with that information made available to the proper authorities. Because of the advantages of a highly calibrated smart antenna, the geo-location of all sources of transmission will be calculated. The presence of a line-of-sight between the mobile unit and the base station is important for smart antennas aiming for geo-location of the wireless communication channel users. The smart antenna systems therefore need a mechanism to calibrate the system as a whole and ensure that it functions within certain specifications.
The advantages of smart antenna systems are described in U.S. Pat. No. 5,515,378. This patent describes the advantages of utilizing the spatial data from various antenna elements to increase the capacity, coverage, and quality of wireless communication networks as well as other benefits that derive from geo-location capability. Further information on geo-location is found in U.S. Pat. No. 5,508,707, which describes a polygonic method for obtaining directional data. This patent explains the needs and the benefits of tracking the location of the mobile unit.
U.S. Pat. No. 5,233,628 is for a computer based bit error simulation method and apparatus used in digital wireless communications. The disclosed simulation allows quantitative testing of digital baseband systems prior to product completion, or as a substitute for field testing. The system utilizes complex algorithms to generate the required test signals. The transmitter section copies and generates the data stream of the unit to be tested, the data stream is manipulated by the bit-error-rate (BER) simulator section, and the receiver section analyzes the bit error rates under simulated conditions.
The invention described in European patent applications 94120494, 94305383, and U.S. Pat. No. 5,602,555, describe a base station arrangement. The three applications are all related and the entire method and apparatus of a smart antenna are disclosed, along with the benefits of such a system.
U.S. Pat. Nos. 5,675,581 and 5,596,570 are related patents and describe a method and apparatus for simulating interference. The systems use a white noise generator and signal processing techniques to generate variable interference components.
None of the prior art reveals or discloses any of the functionality or operating characteristics of the present invention, but instead provides a good background for the present invention and illustrates the need for the present invention.
Thus, multi-sensor antenna arrays, such as the smart antenna system, are a viable alternative for the expansive mobile communications systems. The many advantages of the smart antenna system include maximizing capacity, reducing co-channel interference, eliminating/reducing drop-out and hand-offs, smaller channel bandwidth, fewer base stations, and the ability to locate the user. The smart antenna systems employ sophisticated algorithms to extract the data received from multiple sensor elements and process the data according to minimization criteria and various mathematical calculations.
In conjunction with this highly sophisticated processing, there is a need for comparably sophisticated testing. The present method of testing multi-sensor antenna arrays involves the simulation of a single channel. These present techniques sometimes simulate delay spread or Doppler spread, alone or in combination, but do not combine the effects of delay, Doppler, and angular spread in a multi-channel environment. The prior art testing methods tested various parameters for a single channel and the functionality of the antenna array was judged by these results. This single transmission testing is inadequate to properly test the smart antenna system, and does not account for the practical situations and environmental conditions.
Current testing methods and systems fail to integrate and simulate the effects of delay spread, Doppler spread, and angular spread on the smart antenna processor system. Present calibration/test methods and systems fail to reveal the minimum and maximum threshold levels of the processing capabilities of a smart antenna system installation.
The current methods fail to adequately simulate the conditions of urban and rural environments as well as other interference parameters. Current test systems don't produce the highly calibrated smart antenna systems required for more efficient operations, increased capacity, and overall improved operation.
Current test systems are unable to replicate the dramatically different effects of scattering and multipath fading at different antenna sites. The current testing equipment and methods only test a single antenna element at a time and completely disregards the actual conditions of the antenna in use. What is needed is a methodology and a device for generating multiple source signals that allows for operator control and adjustment of simulated environmental and equipment conditions.